"April 2004" / Bibliography: p. 285-299. / xxviii, 299p : ill., map ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Artificial neural networks (ANNs), trained to make short term forecasts of algal blooms in lakes and rivers, are potentially useful decision making tools for the operational management of eutrophication. This thesis addresses the question of whether a standardised, gemeric ANN model representation can be developed to achieve this goal. It is argued that four requirements need to be addressed: i) compatibility of models with existing water quality monitoring regimes, ii) stability and repeatability of training outcomes, iii) realistic and meaningful estimates of model performance, and iv) explanation of predictions. / Thesis (Ph.D.)--University of Adelaide, School of Earth and Environmental Sciences, Discipline of Environmental Biology, 2004
| Identifer | oai:union.ndltd.org:ADTP/263283 |
| Date | January 2004 |
| Creators | Wilson, Hugh Edward Campbell |
| Source Sets | Australiasian Digital Theses Program |
| Language | en_US |
| Detected Language | English |
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